Sudoku evolution

Jilg, Johannes; Carter, Jenny

doi:10.1109/icegic.2009.5293614

Cited by 8 publications

(9 citation statements)

References 1 publication

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“…Since the popularization of Sudoku, several different stochastic optimization algorithms have been applied to solve these types of puzzles [6], [7]. From among all different metaheuristics, EAs are likely to be the most popular approaches.…”

Section: A Meta-heuristics For Sudokumentioning

confidence: 99%

“…In fact, schemes that do not include any mechanisms to alleviate premature convergence yield quite disappointing results [6], [27]. whereas some of the most effective solvers do in fact include mechanisms to avoid premature convergence.…”

Section: A Meta-heuristics For Sudokumentioning

confidence: 99%

“…When stochastic techniques are used, the solution space is still searched even though a local optimum has been found, allowing for the global optimum to be detected in some cases, so a solution to this problem could lie in using stochastic optimization techniques [5]. In fact, most popular meta-heuristics have been adapted to tackle the Sudoku problem [6], [7]. In the particular case of Evolutionary Algorithms (EAs), its basic variants can solve simple Sudoku puzzles [8], although they encounter significant difficulties when tackling more difficult instances [9], [10], [11].…”

Section: Introductionmentioning

confidence: 99%

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The importance of the individual encoding in memetic algorithms with diversity control applied to large Sudoku puzzles

Segura

Segredo

Miranda

2017

2017 IEEE Congress on Evolutionary Computation (CEC)

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Abstract-In recent years, several memetic algorithms with explicit mechanisms to delay convergence have shown great promise when solving 9x9 Sudoku puzzles. This paper analyzes and extends state-of-the-art schemes for dealing with Sudoku puzzles of larger dimensionality. Two interesting aspects are analyzed: the importance of the encoding and its relation with the way of managing the diversity. Specifically, three different ways of encoding the individuals and six different methods, including four that control the diversity in a special way, are studied. Computational results are shown with twenty 16x16 Sudoku puzzles. Contrary to the low-dimensional case, important differences appear among the several ways of controlling diversity. Specifically, a method that incorporates multi-objective concepts in the replacement phase to deal with the diversity, resulted in the most promising method. Results show that both the encoding and the way of managing diversity are crucial to attain high success probabilities in large Sudoku puzzles. They also show that, while the analyzed encodings induce different search space sizes, this feature is not enough to justify the differences in the performance attained by them.

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Section: A Meta-heuristics For Sudokumentioning

confidence: 99%

Section: A Meta-heuristics For Sudokumentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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The importance of the individual encoding in memetic algorithms with diversity control applied to large Sudoku puzzles

Segura

Segredo

Miranda

2017

2017 IEEE Congress on Evolutionary Computation (CEC)

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“…Mantere and Koljonen followed up their initial work with another study applying genetic algorithms to Sudoku, introducing a number of more advanced algorithm features such as an ageing process and population regeneration [11]. Studies comparing a number of evolutionary algorithms against one another have also been performed [3], [12].…”

Section: A Related Workmentioning

confidence: 99%

“…Jilg and Carter compared genetic algorithms with geometric particle swarm optimisation, bee colony optimisation, artificial immune system, simulated annealing and quantum annealing using Sudoku as the problem of interest [12]. They demonstrated that genetic algorithms performed worse than all other techniques analysed, but admitted that their implementation was not at all optimised and could have yielded drastically different results had they done so.…”

Section: A Related Workmentioning

confidence: 99%

Development and analysis of genetic algorithms: Sudoku case study

Milton

Ortega-Sánchez

2012

TENCON 2012 IEEE Region 10 Conference

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This paper discusses the design and subsequent analysis of software implementing a configurable genetic algorithm. The genetic algorithm is primarily targeted towards the solving of Sudoku puzzles. Sudoku is regarded as an ideal test-bed for algorithm development due to the fact that it is a constrained optimisation problem that belongs to the NP-complete class of computational problems. The aim of this paper is to outline the various features currently implemented in the software, and to present preliminary results of an analysis of various aspects of the underlying genetic algorithm.

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